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Proceedings ArticleDOI

Human motion analysis: a review

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TLDR
The paper gives an overview of the various tasks involved in motion analysis of the human body, and focuses on three major areas related to interpreting human motion: motion analysis involving human body parts, tracking of human motion using single or multiple cameras, and recognizing human activities from image sequences.
Abstract
Human motion analysis is receiving increasing attention from computer vision researchers. This interest is motivated by a wide spectrum of applications, such as athletic performance analysis, surveillance, man-machine interfaces, content-based image storage and retrieval, and video conferencing. The paper gives an overview of the various tasks involved in motion analysis of the human body. The authors focus on three major areas related to interpreting human motion: 1) motion analysis involving human body parts, 2) tracking of human motion using single or multiple cameras, and 3) recognizing human activities from image sequences. Motion analysis of human body parts involves the low-level segmentation of the human body into segments connected by joints, and recovers the 3D structure of the human body using its 2D projections over a sequence of images. Tracking human motion using a single or multiple camera focuses on higher-level processing, in which moving humans are tracked without identifying specific parts of the body structure. After successfully matching the moving human image from one frame to another in image sequences, understanding the human movements or activities comes naturally, which leads to a discussion of recognizing human activities. The review is illustrated by examples.

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Citations
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Patent

Controlling objects via gesturing

TL;DR: In this article, a system and process that controls a group of networked electronic components using a multimodal integration scheme in which inputs from a speech recognition subsystem, gesture recognition subsystem employing a wireless pointing device and pointing analysis subsystem also employing the pointing device, are combined to determine what component a user wants to control and what control action is desired.
Book ChapterDOI

A Literature Review on Video Analytics of Crowded Scenes

TL;DR: This chapter presents a review and systematic comparison of the state of the art on crowd video analysis, divided into two broad categories: the macroscopic and the microscopic modelling approach.

Recognizing Multitasked Activities using Stochastic Context-Free Grammar

Darnell Moore, +1 more
TL;DR: In this article, a model of stochastic context-free grammar is developed using underlying rules of an activity, which provides the structure for recognizing semantically meaningful behavior over extended periods.
Patent

Combined activation for natural user interface systems

TL;DR: In this paper, a plurality of signals received from a user is evaluated to determine whether the plurality is associated with a visual display and a context associated with the visual display may be retrieved.
Journal ArticleDOI

Two-layer hidden Markov model for human activity recognition in home environments

TL;DR: A two-layer HMM is proposed to build a ADLs recognition model that can represent the mapping between low-level sensor data and high-level activity based on the binary sensor data.
References
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Proceedings ArticleDOI

Determining Optical Flow

TL;DR: In this article, a method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image, and an iterative implementation is shown which successfully computes the Optical Flow for a number of synthetic image sequences.
Journal ArticleDOI

Pfinder: real-time tracking of the human body

TL;DR: Pfinder is a real-time system for tracking people and interpreting their behavior that uses a multiclass statistical model of color and shape to obtain a 2D representation of head and hands in a wide range of viewing conditions.
Journal ArticleDOI

Representation and recognition of the spatial organization of three-dimensional shapes.

TL;DR: The human visual process can be studied by examining the computational problems associated with deriving useful information from retinal images by applying the approach to the problem of representing three-dimensional shapes for the purpose of recognition.
Proceedings ArticleDOI

Recognizing human action in time-sequential images using hidden Markov model

TL;DR: The recognition rate is improved by increasing the number of people used to generate the training data, indicating the possibility of establishing a person-independent action recognizer.
Journal ArticleDOI

Visual motion perception.

TL;DR: The author uses projective relations as the theoretical foundation of his investigations of visual space and motion and concludes that during locomotion the components of the human visual environment are interpreted as rigid structures in relative motion.
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